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Emerging Concepts and Innovative TechnologiesHow would you label your submission?
IndustryAbstract
Because synthetic training scenarios are, by definition, event-based (and therefore the activities which occur in them are temporal), they are an ideal use case for illustrating how to apply proairetic narratological methodology to the design of machine-readable xAPI Profiles. This approach will be of value to the modeling and simulation community because it bridges the gap between the descriptive work of non-technical subject matter experts and simulation scenario designers and the instrumentation work of highly-technical simulation software engineers and data scientists. The goal of this methodology will be the design of learning activity data profiles which produce consistent and explainable data.
In this research, “narratological methodology” defines 1) the “plot action” described in the structure of the event-based activities and 2) the context (and therefore the semantic, symbolic, and cultural codes) represented explicitly or implicitly in that description. The conceit of this approach is that there is an affinity between the process of “the describing of a learning experience” and the narratological underpinnings of “the telling of a story”. This design method aligns with the data implementation approach of xAPI — in that the data structure intentionally mirrors human language.
There are two overarching elements to the approach. First, one must describe a learning experience in complete detail and then break down that description into its structural components of syntactic fundamentals, semantic concepts, procedural sequences, and expressed or implied activity patterns. Second, one must disambiguate between how highly generalizable versus how highly contextualized the described set of activities within the learning experience may be. Because this approach is machine-readable and leverages modularizable syntax and semantics, the research has implications on the scalable evaluation of individuals in regard not just to “whether they are trained” but whether their proficiencies carry over to volatility, uncertainty, complexity, and ambiguity in alternate contexts.